Priority setting for health technology assessment at CADTH
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to describe a current practical approach of priority setting of health technology assessment (HTA) research that involves multi-criteria decision analysis and a deliberative process. METHODS: Criteria related to HTA prioritization were identified and grouped through a systematic review and consultation with a selection committee. Criteria were scored through a pair-wise comparison approach. Criteria were pruned based on the average weights obtained from consistent (consistency index < 0.2) responders and consensus. HTA proposals are ranked based on available information and a weighted criteria score. The rank, along with additional contextual information and discussion among committee members, is used to achieve consensus on HTA research priorities. RESULTS: Six of eleven criteria represented > 75 percent of the weight behind committee member decisions to conduct an HTA. These criteria were disease burden, clinical impact, alternatives, budget impact, economic impact, and available evidence. Since May 2006, committees have considered 102 proposals at sixteen biannual in-person advisory committee meetings. These have selected twenty-nine research priorities for the HTA program. CONCLUSIONS: The approach works well and was easy to implement. Feedback from committee members has been positive. This approach may assist HTA and other research agencies in better priority setting by informing the selection of the most important and policy-relevant topics in the presence of a wide variety of research proposals. This may in turn lead to efficiently allocating resources available for HTA research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".